{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>gender</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20110820</td>\n",
       "      <td>20170920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20150628</td>\n",
       "      <td>20170622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20160411</td>\n",
       "      <td>20170712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>20150906</td>\n",
       "      <td>20150907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20170126</td>\n",
       "      <td>20170613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           msno  city  bd gender  \\\n",
       "0  XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=     1   0    NaN   \n",
       "1  UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=     1   0    NaN   \n",
       "2  D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=     1   0    NaN   \n",
       "3  mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=     1   0    NaN   \n",
       "4  q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=     1   0    NaN   \n",
       "\n",
       "   registered_via  registration_init_time  expiration_date  \n",
       "0               7                20110820         20170920  \n",
       "1               7                20150628         20170622  \n",
       "2               4                20160411         20170712  \n",
       "3               9                20150906         20150907  \n",
       "4               4                20170126         20170613  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members = pd.read_csv('members.csv')\n",
    "members.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 查看数据集整体情况，查看类别型特征的取值数目，查看缺失情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34403.000000</td>\n",
       "      <td>34403.000000</td>\n",
       "      <td>34403.000000</td>\n",
       "      <td>3.440300e+04</td>\n",
       "      <td>3.440300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.371276</td>\n",
       "      <td>12.280935</td>\n",
       "      <td>5.953376</td>\n",
       "      <td>2.013994e+07</td>\n",
       "      <td>2.016901e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.243929</td>\n",
       "      <td>18.170251</td>\n",
       "      <td>2.287534</td>\n",
       "      <td>2.954015e+04</td>\n",
       "      <td>7.320925e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-43.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.004033e+07</td>\n",
       "      <td>1.970010e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.012103e+07</td>\n",
       "      <td>2.017020e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.015090e+07</td>\n",
       "      <td>2.017091e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.016110e+07</td>\n",
       "      <td>2.017093e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>1051.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.017023e+07</td>\n",
       "      <td>2.020102e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               city            bd  registered_via  registration_init_time  \\\n",
       "count  34403.000000  34403.000000    34403.000000            3.440300e+04   \n",
       "mean       5.371276     12.280935        5.953376            2.013994e+07   \n",
       "std        6.243929     18.170251        2.287534            2.954015e+04   \n",
       "min        1.000000    -43.000000        3.000000            2.004033e+07   \n",
       "25%        1.000000      0.000000        4.000000            2.012103e+07   \n",
       "50%        1.000000      0.000000        7.000000            2.015090e+07   \n",
       "75%       10.000000     25.000000        9.000000            2.016110e+07   \n",
       "max       22.000000   1051.000000       16.000000            2.017023e+07   \n",
       "\n",
       "       expiration_date  \n",
       "count     3.440300e+04  \n",
       "mean      2.016901e+07  \n",
       "std       7.320925e+03  \n",
       "min       1.970010e+07  \n",
       "25%       2.017020e+07  \n",
       "50%       2.017091e+07  \n",
       "75%       2.017093e+07  \n",
       "max       2.020102e+07  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "msno 34403\n",
      "city 21\n",
      "bd 95\n",
      "gender 3\n",
      "registered_via 6\n",
      "registration_init_time 3862\n",
      "expiration_date 1484\n"
     ]
    }
   ],
   "source": [
    "for i in members.columns:\n",
    "    print(i,len(members[i].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "msno                          0\n",
       "city                          0\n",
       "bd                            0\n",
       "gender                    19902\n",
       "registered_via                0\n",
       "registration_init_time        0\n",
       "expiration_date               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 可视化分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "城市分为22种，其中值为1的情况过多，可视为异常的虚假数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(members['city'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(members['bd'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0       19932\n",
       " 22        751\n",
       " 27        750\n",
       " 24        740\n",
       " 26        719\n",
       " 25        716\n",
       " 23        712\n",
       " 28        688\n",
       " 21        685\n",
       " 29        661\n",
       " 20        631\n",
       " 30        602\n",
       " 19        507\n",
       " 31        491\n",
       " 32        466\n",
       " 18        466\n",
       " 33        416\n",
       " 34        404\n",
       " 17        398\n",
       " 35        380\n",
       " 36        341\n",
       " 37        300\n",
       " 38        294\n",
       " 39        226\n",
       " 16        215\n",
       " 40        204\n",
       " 41        194\n",
       " 44        138\n",
       " 42        131\n",
       " 43        121\n",
       "         ...  \n",
       " 102         2\n",
       " 131         1\n",
       " 78          1\n",
       " 85          1\n",
       " 3           1\n",
       " 2           1\n",
       " 1051        1\n",
       " 97          1\n",
       " 144         1\n",
       " 93          1\n",
       " 96          1\n",
       "-43          1\n",
       " 82          1\n",
       " 931         1\n",
       " 106         1\n",
       " 76          1\n",
       " 87          1\n",
       " 101         1\n",
       " 90          1\n",
       " 70          1\n",
       " 1030        1\n",
       " 7           1\n",
       " 12          1\n",
       " 103         1\n",
       "-38          1\n",
       " 89          1\n",
       " 107         1\n",
       " 10          1\n",
       " 11          1\n",
       " 95          1\n",
       "Name: bd, Length: 95, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.bd.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in members[members.bd<3].index:\n",
    "    members.loc[i,'bd'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>city</th>\n",
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       "      <th>registration_init_time</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>4aRFGkKzGUpqTbjojytAtRQNdx0egL02hjJhSqq0+NA=</td>\n",
       "      <td>1</td>\n",
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       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>20160216</td>\n",
       "      <td>20170913</td>\n",
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       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>VacLAYo8rJVzIROYARLaqjSupA3Kjd94i61f2N2+NTA=</td>\n",
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       "      <td>20170219</td>\n",
       "      <td>20170611</td>\n",
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       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>VMzoGuN+bJZ6TQ6CLNpYph9zSBzHQIsbfLsIi7sXfHk=</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>20151107</td>\n",
       "      <td>20151114</td>\n",
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       "    <tr>\n",
       "      <th>3678</th>\n",
       "      <td>awRUhQIBoAjbCACIt2urMTH0FiCpYeO8n/iL18cyk3c=</td>\n",
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       "      <td>105</td>\n",
       "      <td>male</td>\n",
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       "      <td>20100930</td>\n",
       "      <td>20170509</td>\n",
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       "    <tr>\n",
       "      <th>4862</th>\n",
       "      <td>xqgEP35JJSgc9lyaFlhMQbUOor9RN5D5RDI/9GNT0mI=</td>\n",
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       "      <td>90</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>20150819</td>\n",
       "      <td>20161127</td>\n",
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       "      <td>WxZltw7ZvYmWAYbpeHbVgTz4eCedFZNXm9WeMyAA6PM=</td>\n",
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       "      <td>131</td>\n",
       "      <td>female</td>\n",
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       "      <th>8885</th>\n",
       "      <td>zuI6CpX1icQHcD89+y3rJ7E9faM/oOWjpfUEK/hJrjE=</td>\n",
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       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>20160610</td>\n",
       "      <td>20161211</td>\n",
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       "    <tr>\n",
       "      <th>8998</th>\n",
       "      <td>NToiKiPwJSP5Gy37os7r7qkYTovuBtl3RIW/Lf+97UQ=</td>\n",
       "      <td>22</td>\n",
       "      <td>931</td>\n",
       "      <td>male</td>\n",
       "      <td>9</td>\n",
       "      <td>20111017</td>\n",
       "      <td>20171020</td>\n",
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       "    <tr>\n",
       "      <th>10205</th>\n",
       "      <td>lzHwXLkh3mIRE1xukTREe4wCWv8D+2xgvX/hcGJYjJ8=</td>\n",
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       "      <th>10786</th>\n",
       "      <td>df6MZe2SAHl5hpik8aWi+cUi6BiTdLP3P8N+UGsgY2o=</td>\n",
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       "      <td>105</td>\n",
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       "      <td>3</td>\n",
       "      <td>20140613</td>\n",
       "      <td>20170809</td>\n",
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       "    <tr>\n",
       "      <th>11899</th>\n",
       "      <td>/qAE+55Dq6nug+IayYlcnTXzGXdxRfywUAYXnmT1uj4=</td>\n",
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       "      <td>144</td>\n",
       "      <td>male</td>\n",
       "      <td>9</td>\n",
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       "      <td>mMIYEex2kjyrSlvKhFBTYujAtb1EiZg9DWXHfjbS4N4=</td>\n",
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       "      <td>4</td>\n",
       "      <td>20170201</td>\n",
       "      <td>20170204</td>\n",
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       "    <tr>\n",
       "      <th>13363</th>\n",
       "      <td>VqO7MBw60BCuCL9+6z7hFnPmn09OZTnhDehFkOHYW9s=</td>\n",
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       "      <td>20110816</td>\n",
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       "    <tr>\n",
       "      <th>15905</th>\n",
       "      <td>0QqQJKwvsz2EfQ6dTCVXvXqfctMAvcJ3RXKqLihwgh4=</td>\n",
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       "      <td>20160911</td>\n",
       "      <td>20170812</td>\n",
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       "    <tr>\n",
       "      <th>17621</th>\n",
       "      <td>oaVjHRPotHHxObuV9VQoPgnAVT7sx8Mm1+OQeY5cJk8=</td>\n",
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       "      <td>20161117</td>\n",
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       "    <tr>\n",
       "      <th>20069</th>\n",
       "      <td>MbcmawIXRcpz3c1BiTABuZumIKJh+gbFn/SMILtT8Do=</td>\n",
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       "      <td>1051</td>\n",
       "      <td>female</td>\n",
       "      <td>7</td>\n",
       "      <td>20110314</td>\n",
       "      <td>20170926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24113</th>\n",
       "      <td>36o5fTui2UexKNVfAc2GF8iqQ3MAa2hUaDUxz+PFTko=</td>\n",
       "      <td>15</td>\n",
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       "      <td>3</td>\n",
       "      <td>20120116</td>\n",
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       "    <tr>\n",
       "      <th>25661</th>\n",
       "      <td>PZv9BpjrYdEDAve/vN2gETpp+PsAok9i1BMBnakD2xY=</td>\n",
       "      <td>13</td>\n",
       "      <td>111</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>20150914</td>\n",
       "      <td>20171219</td>\n",
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       "    <tr>\n",
       "      <th>27217</th>\n",
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       "      <td>vbSj3dv4yw2WPUvftIKYTvBMGBv0L9EHB/39LfM5Muw=</td>\n",
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       "      <td>4</td>\n",
       "      <td>20170125</td>\n",
       "      <td>20170128</td>\n",
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       "    <tr>\n",
       "      <th>28521</th>\n",
       "      <td>yQXy9RWh+FHXHDbo9/cYT5vTTq+UdkcI8oNOpOE6MgY=</td>\n",
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       "      <td>3</td>\n",
       "      <td>20140929</td>\n",
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       "      <td>7</td>\n",
       "      <td>20110809</td>\n",
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       "    <tr>\n",
       "      <th>29876</th>\n",
       "      <td>FvR1Fl2kfw6P/YSnX1vsM8VQRe65rj7yOp1zKMHtrsY=</td>\n",
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       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>20160806</td>\n",
       "      <td>20170122</td>\n",
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       "    <tr>\n",
       "      <th>30460</th>\n",
       "      <td>ZgPrU8gMY28iwnBPqRzDpC2K2xYytky/rsjOpSO/1Ys=</td>\n",
       "      <td>17</td>\n",
       "      <td>105</td>\n",
       "      <td>male</td>\n",
       "      <td>9</td>\n",
       "      <td>20081212</td>\n",
       "      <td>20171015</td>\n",
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       "    <tr>\n",
       "      <th>30914</th>\n",
       "      <td>rAfVMTall1862LdvIdQm0ejmtmsRVirQnX99wuqqY/8=</td>\n",
       "      <td>22</td>\n",
       "      <td>107</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>20150420</td>\n",
       "      <td>20171129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32399</th>\n",
       "      <td>W5qo/kJ4p5mF2CFcUOEwTYw3xb71kjuBAze0t0gc6Tc=</td>\n",
       "      <td>15</td>\n",
       "      <td>112</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>20150503</td>\n",
       "      <td>20161013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34045</th>\n",
       "      <td>7FzvowIvKSs4fI0hldjz3fRFar74AYT4GE3DhbmvXso=</td>\n",
       "      <td>13</td>\n",
       "      <td>105</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>20161230</td>\n",
       "      <td>20170102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               msno  city    bd  gender  \\\n",
       "511    4aRFGkKzGUpqTbjojytAtRQNdx0egL02hjJhSqq0+NA=     1   103  female   \n",
       "1117   VacLAYo8rJVzIROYARLaqjSupA3Kjd94i61f2N2+NTA=    12   101    male   \n",
       "2287   VMzoGuN+bJZ6TQ6CLNpYph9zSBzHQIsbfLsIi7sXfHk=     1    97    male   \n",
       "3678   awRUhQIBoAjbCACIt2urMTH0FiCpYeO8n/iL18cyk3c=    12   105    male   \n",
       "4862   xqgEP35JJSgc9lyaFlhMQbUOor9RN5D5RDI/9GNT0mI=     1    90    male   \n",
       "7453   WxZltw7ZvYmWAYbpeHbVgTz4eCedFZNXm9WeMyAA6PM=    15   131  female   \n",
       "8885   zuI6CpX1icQHcD89+y3rJ7E9faM/oOWjpfUEK/hJrjE=    22    89    male   \n",
       "8998   NToiKiPwJSP5Gy37os7r7qkYTovuBtl3RIW/Lf+97UQ=    22   931    male   \n",
       "10205  lzHwXLkh3mIRE1xukTREe4wCWv8D+2xgvX/hcGJYjJ8=     4   105    male   \n",
       "10786  df6MZe2SAHl5hpik8aWi+cUi6BiTdLP3P8N+UGsgY2o=     5   105  female   \n",
       "11899  /qAE+55Dq6nug+IayYlcnTXzGXdxRfywUAYXnmT1uj4=    21   144    male   \n",
       "12749  mMIYEex2kjyrSlvKhFBTYujAtb1EiZg9DWXHfjbS4N4=     5   112    male   \n",
       "13363  VqO7MBw60BCuCL9+6z7hFnPmn09OZTnhDehFkOHYW9s=    15    96  female   \n",
       "13771  +Pf237ESMeXEWzGCJvAonB1qkodzBLfOhuIK9nM82g4=    15   112    male   \n",
       "15905  0QqQJKwvsz2EfQ6dTCVXvXqfctMAvcJ3RXKqLihwgh4=     1   102  female   \n",
       "17621  oaVjHRPotHHxObuV9VQoPgnAVT7sx8Mm1+OQeY5cJk8=     6   102    male   \n",
       "20069  MbcmawIXRcpz3c1BiTABuZumIKJh+gbFn/SMILtT8Do=    13  1051  female   \n",
       "24113  36o5fTui2UexKNVfAc2GF8iqQ3MAa2hUaDUxz+PFTko=    15    95    male   \n",
       "25661  PZv9BpjrYdEDAve/vN2gETpp+PsAok9i1BMBnakD2xY=    13   111  female   \n",
       "27217  B/LNCpdhgxCDPl25UodsgeoN6TWcf4QHhT+e3JFRKzg=    22    93    male   \n",
       "28099  vbSj3dv4yw2WPUvftIKYTvBMGBv0L9EHB/39LfM5Muw=     9   111    male   \n",
       "28521  yQXy9RWh+FHXHDbo9/cYT5vTTq+UdkcI8oNOpOE6MgY=     1   111    male   \n",
       "29619  aOZpMN6/L7jMcanVXCi17bI30WrFGoNWUYZS4xylzBM=    13  1030  female   \n",
       "29876  FvR1Fl2kfw6P/YSnX1vsM8VQRe65rj7yOp1zKMHtrsY=     1   106    male   \n",
       "30460  ZgPrU8gMY28iwnBPqRzDpC2K2xYytky/rsjOpSO/1Ys=    17   105    male   \n",
       "30914  rAfVMTall1862LdvIdQm0ejmtmsRVirQnX99wuqqY/8=    22   107    male   \n",
       "32399  W5qo/kJ4p5mF2CFcUOEwTYw3xb71kjuBAze0t0gc6Tc=    15   112  female   \n",
       "34045  7FzvowIvKSs4fI0hldjz3fRFar74AYT4GE3DhbmvXso=    13   105    male   \n",
       "\n",
       "       registered_via  registration_init_time  expiration_date  \n",
       "511                 3                20160216         20170913  \n",
       "1117                4                20170219         20170611  \n",
       "2287                3                20151107         20151114  \n",
       "3678                9                20100930         20170509  \n",
       "4862                3                20150819         20161127  \n",
       "7453                7                20101216         20170916  \n",
       "8885                4                20160610         20161211  \n",
       "8998                9                20111017         20171020  \n",
       "10205               9                20080105         20080707  \n",
       "10786               3                20140613         20170809  \n",
       "11899               9                20110202         20170930  \n",
       "12749               4                20170201         20170204  \n",
       "13363               9                20110816         20171005  \n",
       "13771               3                20160111         20171204  \n",
       "15905               4                20160911         20170812  \n",
       "17621               4                20161117         20161215  \n",
       "20069               7                20110314         20170926  \n",
       "24113               3                20120116         20170708  \n",
       "25661               3                20150914         20171219  \n",
       "27217               3                20150822         20170613  \n",
       "28099               4                20170125         20170128  \n",
       "28521               3                20140929         20170615  \n",
       "29619               7                20110809         20170915  \n",
       "29876               4                20160806         20170122  \n",
       "30460               9                20081212         20171015  \n",
       "30914               3                20150420         20171129  \n",
       "32399               3                20150503         20161013  \n",
       "34045               3                20161230         20170102  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members[members.bd>88]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
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       "      <th>gender</th>\n",
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       "      <th>511</th>\n",
       "      <td>4aRFGkKzGUpqTbjojytAtRQNdx0egL02hjJhSqq0+NA=</td>\n",
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      "text/plain": [
       "                                             msno  city  bd  gender  \\\n",
       "511  4aRFGkKzGUpqTbjojytAtRQNdx0egL02hjJhSqq0+NA=     1 NaN  female   \n",
       "\n",
       "     registered_via  registration_init_time  expiration_date  \n",
       "511               3                20160216         20170913  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将大于88岁的值改为0\n",
    "for i in members[members.bd>88].index:\n",
    "    members.loc[i,'bd'] = 0\n",
    "members.replace(0, value = np.nan,inplace = True)\n",
    "members[members.msno == '4aRFGkKzGUpqTbjojytAtRQNdx0egL02hjJhSqq0+NA=']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "sns.distplot(members[members.bd>0]['bd'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,6))\n",
    "sns.countplot(members[members.bd>0]['bd'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于大于88岁甚至上百岁的人听歌的情况太特别，设为异常值0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 性别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(members.gender)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.gender.fillna('unknown',inplace = True)\n",
    "members.gender.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      7405\n",
       "female    7096\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 注册方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(members['registered_via'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 注册时间&到期时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eLXu/LZVVrRMRg8BeoGWEtrXKW4A9KUatfUFx9PG31Tor6SZJPZJ6+vr6Rh2cmZnly0kaqlIWmXUmq/z4jqRfADqAT1SpS0TcGxEdEdHR2tparYqZmY1TziW324DFZe/PA7bXqLNNUiNwNrBrlLbVyncC8yU1pqONE/Yl6aeA3wZ+IiL6M/puZmaTKOdIYz3Qlq5qaqI4sd1dUacbuD69XgE8FhGRyjvT1VVLgTbg6VoxU5vHUwxSzLUAkt4B3ANcGxE7xjdcMzObiFGPNCJiUNItwKNACbg/IjZIugPoiYhu4D7gQUm9FEcYnantBkmPABuBQeDmiBgCqBYz7fJWoEvSnRRXTN2Xyj8BzAW+UJwvZ2tEXDvh34CZmWVT8eX+9NTR0RE9PT3T3Q0zs2Nq3fldy3TcES7pmYjoqLbNd4SbmdWBbbsP8bVv1f8Vn04aZmZ14Nmtu3l0w3ep99kfJw0zszpwsH+IAPoHj053V0bkpGFmVgcODhT3NB8+MjTNPRmZk4aZWR041F8kizecNMzMbDQH+4sjjTcGnDTMzGwEEeHpKTMzy3P4yFGOpoumPD1lZmYjGj7KAE9PmZnZKA71lyWNI77k1szMRnCw7OjC01NmZjai4SunSg3yiXAzMxvZ8JHGObObfE7DzMxGdrB/kBklMW9Wo6enzMxsZIcGBpnT1MisGSUnDTMzG9nB/iHmNBdJw+c0zMxsRAcHBpnTXCqONHxOw8zMRnKwf5DZTY3MaioxeDQ4MlS/92o4aZiZTaGjEQwdPfFBSwcHhpjTVGLmjBJQ3+tPOWmYmU2htc9v54EnXzn2/sjQUQYGjx47pwH1vZRIVtKQtEzSJkm9km6rsr1Z0sNp+zpJS8q23Z7KN0m6erSYkpamGC+nmE2p/L2SnpU0KGnFRAZtZjZdvrv3Dba+foij6bGuh1KCmJOmp6C+7wofNWlIKgF3AdcA7cBKSe0V1W4EdkfEhcBqYFVq2w50AhcBy4C7JZVGibkKWB0RbcDuFBtgK3AD8ND4hmpmNv329w8yMHSUfW8cAY7fDT58IhxO/empy4DeiNgcEQNAF7C8os5y4IH0eg1wpSSl8q6I6I+ILUBvilc1ZmpzRYpBinkdQES8EhEvAPV7hsjMbAQRwf7DRZLoO9APHF/hdnZT47FzGqf0kQawCHi17P22VFa1TkQMAnuBlhHa1ipvAfakGLX2NSJJN0nqkdTT19c3lqZmZifV3jeOHDsJ3rc/JY30mNc5zWXTU6f4OQ1VKYvMOpNVni0i7o2IjojoaG1tHUtTM7OTakdKFFCeNI5PT82cUXwk1/Py6DlJYxuwuOz9ecD2WnUkNQJnA7tGaFurfCcwP8WotS8zs1PSjn1FomjQ8aRxaGCQBsHMGSUaGxqYUarvlW5zksZ6oC1d1dREcWK7u6JON3B9er0CeCwiIpV3pqurlgJtwNO1YqY2j6cYpJhrxz88M7P6sWP/YQAWL5h9/JxG/xCzmhppUDHRUu93hY+aNNL5hVuAR4GXgEciYoOkOyRdm6rdB7RI6gU+DNyW2m4AHgE2An8H3BwRQ7Vipli3Ah9OsVpSbCRdKmkb8LPAPZKG65uZnRKGjy7e3DqH/YcHOXxkqFhCJJ3LgOKIo55PhDeOXgUi4m+Av6ko+52y14cpPsyrtf0Y8LGcmKl8M8XVVZXl6ymmq8zMTkk79vfTVGrgvAWzgSKJDC9WOGxWU30nDd8RbmY2RXbs72fuzEZa5zYDKWlUHGnU+0q3ThpmZlNkx77DzJvZyII5TZQk+g70c6h/kNnlRxp1Pj3lpGFmNkX69vczb+YMSg3inLlNfG/fYQ4NDDGn6XjSmNl0ip8INzOzybFjfz/zZhYJonVuM1t3HSIo7tEYNmtGif7Bo8fWpqo3ThpmZlPg0MAgB/oHOStNRZ07r/mExQqH1fv6U04aZmZTYPhy23kzZwDQOq/52LY5zd+fNOp1ispJw8xsCgwvITJ3eHrqhKRRNj1V58ujO2mYmU2B4SVEhs9pLJx7PGnMLj8Rfmx6qj7Xn3LSMDObAsNLiAxPT82cUeKslEAq79OA+j3SyLoj3MzMxuahdVtPeP+VTX00CGaXJYiF85rpHzxKY+n49/d6Xx7dScPMbArsP3yEeTNnHFuYEKD9TWedcOUUcGx59Hq9espJw8xsCuw/PHjsfMawd79lIe9+y4n1mkoNNKh+p6d8TsPMbArsPzzI3ObRv6dLquvl0Z00zMymwPD0VI56Xh7dScPM7CQbOhocHBj6vumpWmY11e9Kt04aZmYn2YH0HPDspOEjDTOzM9f+w0cAOGss01M+p2Fmdmbaf3iMRxp1/PQ+Jw0zs5PgO68f5Hv7irvA96UjjZyrp+D40/uiDpdHd9IwM5tkT7y8kz/5+mY+/dVv88rOgxxIRxpzM480Fs5t5mjAF194jaNH6ytxZI1A0jLgk0AJ+ExEfLxiezPwWeAS4HXg/RHxStp2O3AjMAR8KCIeHSmmpKVAF3AO8CzwixExMNI+zMymS+VyId/dd5h7vvptWuc1M3Q0+NN/fIVzz2pmdlOJxoa87+nvOH8+39t3mCd6d/IbjzzPJ1b8GE2N9fEdf9SkIakE3AX8W2AbsF5Sd0RsLKt2I7A7Ii6U1AmsAt4vqR3oBC4Cfgj4B0lvTW1qxVwFrI6ILkmfTrE/VWsfE/0FmNn0iQhUtqxGRLDr4ACv7S2epf2ms2cd+7A8NDDI9/b1M6Mkzp0384QP0WIqp1iCozLe0YBSw/GyvYeO0Nt3gF0HB7igZTZLWuYwoyS27jpEzyu76e07wNt+YB4dSxawaP4stu89zLrNr/Piv+xlScscLn/zObz13Hls3/sGX/vWTrrWb+WsmTO48Ny5tMxp4oF/fIWmxgau//EllBrEfU9sYdvuN/jBs2Zm/14aJK55+w8yp6nE2ue3s2NfP++/dDGXXLCA8xbMOmGMUy3nSOMyoDciNgNI6gKWA+VJYznwu+n1GuCPVYxqOdAVEf3AFkm9KR7VYkp6CbgC+ECq80CK+6la+4iTMOn3+ae3cs9Xvz3ZYc1OS0MRDA4FR4aCoxE0NogZpQYaGqD/yFHeODLE4FDQ1NjAzBkNzCg1cPjIEAf6Bzl85ChNjQ3MbW5k1owSOw/00z94fElwqZiqOXxk6NjJ5GEL5zbRILHnjSMMpDZNpQbOnj2DGQ1if/8gB/sHORrFIoFzmxsZOhq8fnDghDilBjG3uZG9bxTnHQQMf6g0NzYc609jgxhMU0VNpQYGhoryec2NHDoyxBO9O49tu+m9b2b+7CYAPvhv3syffmMLPzCGpFGMXfzE285lTnMjf/3iazy5+XWgGMuMUvE7G06GwfGPQVGU/eZVb2X5xYvGtM8cOUljEfBq2fttwOW16kTEoKS9QEsqf6qi7fAoqsVsAfZExGCV+rX2sbO8I5JuAm5Kbw9I2pQxxpEsrNzHac7jPb2dcuN9pUb5d/KaT8t4P/L71cv/6uTv+th4v37rhOJcUGtDTtKodhxU+e2+Vp1a5dUm50aqn9sPIuJe4N4qdcdFUk9EdExWvHrn8Z7ePN7T21SMN+fMyjZgcdn784DttepIagTOBnaN0LZW+U5gfopRua9a+zAzsymSkzTWA22Slkpqojix3V1Rpxu4Pr1eATyWzjV0A52SmtNVUW3A07VipjaPpxikmGtH2YeZmU2RUaen0vmDW4BHKS6PvT8iNki6A+iJiG7gPuDBdKJ7F0USINV7hOKk+SBwc0QMAVSLmXZ5K9Al6U7guRSbWvuYApM21XWK8HhPbx7v6e2kj1f+sm5mZrnq424RMzM7JThpmJlZtjM2aUi6X9IOSd+ssX2BpL+U9IKkpyW9PZW/TdLzZT/7JP361PZ+7MY73rTtNyRtkPRNSZ+XNLa7lKbBBMf7a2msG06Fvy2ApMWSHpf0Uur3r1WpI0n/V1JvGvc7y7ZdL+nl9HN9Zdt6Mwnj/TtJeyR9cWp7Pj4TGa+kiyU9mdq9IGliK2lExBn5A7wXeCfwzRrbPwH8j/T6h4EvV6lTAr4LXDDd4zlZ46W4qXILMCu9fwS4YbrHcxLH+3bgm8BsigtF/gFom+7xZIz3TcA70+t5wLeA9oo6/w74W4p7nt4FrEvl5wCb038XpNcLpntMJ2u8aduVwH8AvjjdY5mCv+9bh/8NUyzn9Bowf7x9OWOPNCLia4x8n0c78OVU95+BJZJ+oKLOlcC3IyLz5tTpM8HxNgKz0v0xs/n++3TqzgTG+yPAUxFxKIqVCb4K/MzJ7u9ERcRrEfFser0feInjqykMWw58NgpPUdwT9SbgauBLEbErInYDXwKWTWH3x2yC4yUivgzsn8o+T8RExhsR34qIl1Pb7cAOoHW8fTljk0aGfwL+I4Ckyyhuqz+vok4n8Pkp7tfJUnW8EfEvwP8GtlJ8Q9kbEX8/bb2cPLX+vt8E3iupRdJsim9vi2tGqUOSlgDvANZVbKq2JNCiEcpPCeMY7yltIuNN/9abgHEvruekUdvHgQWSngd+leKekWMrpqWbEq8FvjA93Zt0VccraQHFN5ilFIe2cyT9wvR1c9JUHW9EvESxgvKXgL+jSC6DNaPUGUlzgT8Hfj0i9lVurtJktOV76to4x3vKmsh401HWg8AvRcTRKnWz5D0R5AyU/iC/BMUJJop5/S1lVa4Bno2I701D9ybdCOO9GtgSEX1p218A7wb+bJq6OilG+vtGxH2km0ol/T7FN7a6J2kGxQfK5yLiL6pUGWlZn5+sKP/Kyenl5JnAeE9JExmvpLOAvwY+mqauxs1HGjVImp+OJgA+CHytIrOv5PSZmhppvFuBd0manT5cr6SYTz2ljfT3lXRu+u/5FFNYdf93Tn+b+4CXIuIPa1TrBv5zusrmXRRTja9RrMxwVbqibAFwVSqrWxMc7ylnIuNN/87/kuJ8x4RnRs7YIw1Jn6f4drVQ0jbgfwAzACLi0xQnRD8raYhiGZQby9rOpniA1H+d4m6P23jHGxHrJK2heIriIMU0Tt0vzTCRvy/w55JagCMUS9/snsq+j9N7gF8EXkxTbgD/HTgfjo35byjO0fQCh0hHWhGxS9LvUawJB3BHRNT7YqDjHi+ApK9TXDU3N/37uDHSU0Xr1ETG+3MUVxO2SLohld0QEcNxxsTLiJiZWTZPT5mZWTYnDTMzy+akYWZm2Zw0zMwsm5OGmdlpQqMs1FlRd7WOL7z6LUl7svbhq6fMzE4Pkt4LHKC4J+Pto9Uva/erwDsi4r+MVtdHGmYZJH1GUvsY29wg6YcmEmOM+zswyvb5kv7bydq/Tb9qC3VKeouKpeCfkfR1ST9cpWn2zcpOGmYZIuKDEbGxslxSaYRmN1Cs1zVijCk0H3DSOPPcC/xqRFwC/BZwd/lGSRdQrC33WE4wJw077Un6BRUPWnpe0j2SLlDxsKGFkhrSt6+rJC2R9M+SHlDxsJo16e5/JH1FUkd6fUDSHZLWAT8u6XckrVfx4KZ70zIOK4AO4HNpv7MqYqyU9GJqs6qsrwckfUzSP0l6St+/HH/5uJaqeLjO+nRH93D5XElflvRs2sfytOnjwFtSfz6R6n4ktX9B0v+c5F+9TTMVCxy+G/hCupP8Hopnc5TrBNZExFBOTCcNO61J+hHg/cB7IuJiYAj4CYqVbD8N/CawsWy597cB90bEjwL7qP7NfA7Fw50uj4gngD+OiEvTHPIs4KcjYg3QA/x8RFwcEW+U9emH0v6vAC4GLpV0XVnspyLix4CvAb88wvA+CXwqIi6leBjYsMPAz0TEO4H3Af8nrV10G8XzXy6OiI9IugpoAy5L/bgkzYnb6aMB2JP+5sM/P1JRZ0yPeHDSsNPdlcAlwPr0TetK4M0R8RmKJ6D9CsUh+7BXI+Ib6fWfAf+6SswhitVGh71P0jpJL1IkgotG6dOlwFcioi896OlzFGsDAQwAw48gfQZYMkKc93D8f/YHy8oF/L6kFyiePLgIqHbEclX6eY5ibbEfpkgidppIi3BukfSzcOyRsD82vF3S2yie1vhkbswzdsFCO2MIeCAibj+hsJh2Gn6o1lyOP8Wt8nLCapcXHh4+lFfxvPS7gY6IeFXS7wKjPf0nK9AAAAGMSURBVEO92nMPhh2J45c0DjH6/6PV+vfzFE9muyQijkh6pUafBPyviLhnlH3YKaLGQp0/D3xK0kcpFu3sonhODBQnwLvK/s2NyknDTndfBtZKWh0ROySdQ3GE8VsU3/C/A/wJ8NOp/vmSfjwinqT4H+qJUeIPfxjvTPPHK4A1qWx/2leldcAnJS0Edqf9/NE4xvYNiqmFP6P4YBh2NrAjJYz3UTyVsFp/HgV+T9LnIuKApEUUSWvHOPpidSAiVtbYVPXxvRHxu2Pdh6en7LSWrlb6KPD3abrmSxRTPpcCqyLic8CApOFlpF8Crk91zwE+NUr8PRRJ50Xgrzi+vDjAnwKfHj4RXtbmNeB24HGKb3zPRsTacQzv14CbJa2nSBTDPgd0SOqhSCb/nPb7OvCNdPL9E+k8zkPAk2lqbQ3Vk5zZMb65zyxR8ezlL47lpiizM42PNMzMLJuPNMzqnKTfBn62ovgLEfGx6eiPndmcNMzMLJunp8zMLJuThpmZZXPSMDOzbE4aZmaW7f8DFuGpL3ex3x4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(members['expiration_date'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(pd.to_datetime(members['expiration_date'],format = '%Y%m%d',errors = 'ignore').dt.year)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2017, 2015, 2016, 2018, 2013, 2012, 2011, 2014, 2019, 2005, 2008,\n",
       "       2006, 2009, 2004, 2007, 2020, 2010, 1970], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expiration_years =  pd.to_datetime(members['expiration_date'],format = '%Y%m%d',errors = 'ignore').dt.year.unique()\n",
    "expiration_years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(pd.to_datetime(members['registration_init_time'],format = '%Y%m%d',errors = 'ignore').dt.year)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>gender</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16867</th>\n",
       "      <td>1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>20140501</td>\n",
       "      <td>19700101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               msno  city  bd gender  \\\n",
       "16867  1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=     1 NaN    NaN   \n",
       "\n",
       "       registered_via  registration_init_time  expiration_date  \n",
       "16867               9                20140501         19700101  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members[members.expiration_date < members.registration_init_time]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>song_id</th>\n",
       "      <th>source_system_tab</th>\n",
       "      <th>source_screen_name</th>\n",
       "      <th>source_type</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2487533</th>\n",
       "      <td>1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=</td>\n",
       "      <td>WznMG5LmzE4k7q1OQLPAV2s96k8ZIrVvG/rihErlYWk=</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487534</th>\n",
       "      <td>1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=</td>\n",
       "      <td>DdKsqy3JAygpcHwihcjBKzzp8SDYhdtXbEZmhKDrOSo=</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2487535</th>\n",
       "      <td>1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=</td>\n",
       "      <td>xEjg9Bs0QcYD3BBQrzPUk89Eb2jBCWu/aki+pOy6H0w=</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 msno  \\\n",
       "2487533  1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=   \n",
       "2487534  1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=   \n",
       "2487535  1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=   \n",
       "\n",
       "                                              song_id source_system_tab  \\\n",
       "2487533  WznMG5LmzE4k7q1OQLPAV2s96k8ZIrVvG/rihErlYWk=               NaN   \n",
       "2487534  DdKsqy3JAygpcHwihcjBKzzp8SDYhdtXbEZmhKDrOSo=               NaN   \n",
       "2487535  xEjg9Bs0QcYD3BBQrzPUk89Eb2jBCWu/aki+pOy6H0w=               NaN   \n",
       "\n",
       "        source_screen_name source_type  target  \n",
       "2487533                NaN         NaN       0  \n",
       "2487534                NaN         NaN       0  \n",
       "2487535                NaN         NaN       0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "train[train.msno == '1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "msno                      1Y+bNz3FxSoJnKOcR/Q8VJGXZbWIstrW0HfBe5LZzKA=\n",
       "city                                                                 1\n",
       "bd                                                                 NaN\n",
       "gender                                                             NaN\n",
       "registered_via                                                       9\n",
       "registration_init_time                                        20140501\n",
       "expiration_date                                               20140501\n",
       "Name: 16867, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.loc[16867,'expiration_date'] = 20140501\n",
    "members.loc[16867]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "到期时间的年份取值有17种"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.to_datetime(members['expiration_date'],format = '%Y%m%d',errors = 'ignore').dt.year.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只取时间的年月，有137种取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.to_datetime(members['expiration_date'],format = '%Y%m%d',errors = 'ignore').apply(lambda x:datetime.strftime(x,'%Y-%m')).unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 增加一部分特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>gender</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>expiration_date_year</th>\n",
       "      <th>expiration_date_month</th>\n",
       "      <th>expiration_date_Ym</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20110820</td>\n",
       "      <td>20170920</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>201709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20150628</td>\n",
       "      <td>20170622</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>201706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20160411</td>\n",
       "      <td>20170712</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>201707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>20150906</td>\n",
       "      <td>20150907</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>201509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20170126</td>\n",
       "      <td>20170613</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>201706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           msno  city  bd gender  \\\n",
       "0  XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=     1 NaN    NaN   \n",
       "1  UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=     1 NaN    NaN   \n",
       "2  D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=     1 NaN    NaN   \n",
       "3  mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=     1 NaN    NaN   \n",
       "4  q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=     1 NaN    NaN   \n",
       "\n",
       "   registered_via  registration_init_time  expiration_date  \\\n",
       "0               7                20110820         20170920   \n",
       "1               7                20150628         20170622   \n",
       "2               4                20160411         20170712   \n",
       "3               9                20150906         20150907   \n",
       "4               4                20170126         20170613   \n",
       "\n",
       "   expiration_date_year  expiration_date_month expiration_date_Ym  \n",
       "0                  2017                      9             201709  \n",
       "1                  2017                      6             201706  \n",
       "2                  2017                      7             201707  \n",
       "3                  2015                      9             201509  \n",
       "4                  2017                      6             201706  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members['expiration_date_year'] = pd.to_datetime(members['expiration_date'],format = '%Y%m%d',errors = 'ignore').dt.year\n",
    "members['expiration_date_month'] = pd.to_datetime(members['expiration_date'],format = '%Y%m%d',errors = 'ignore').dt.month\n",
    "members['expiration_date_Ym'] = pd.to_datetime(members['expiration_date'],format = '%Y%m%d',errors = 'ignore').apply(lambda x:datetime.strftime(x,'%Y%m'))\n",
    "\n",
    "members.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>gender</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>expiration_date_year</th>\n",
       "      <th>expiration_date_month</th>\n",
       "      <th>expiration_date_Ym</th>\n",
       "      <th>use_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20110820</td>\n",
       "      <td>20170920</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>201709</td>\n",
       "      <td>2223 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20150628</td>\n",
       "      <td>20170622</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>201706</td>\n",
       "      <td>725 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20160411</td>\n",
       "      <td>20170712</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>201707</td>\n",
       "      <td>457 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>20150906</td>\n",
       "      <td>20150907</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>201509</td>\n",
       "      <td>1 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20170126</td>\n",
       "      <td>20170613</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>201706</td>\n",
       "      <td>138 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           msno  city  bd gender  \\\n",
       "0  XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=     1 NaN    NaN   \n",
       "1  UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=     1 NaN    NaN   \n",
       "2  D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=     1 NaN    NaN   \n",
       "3  mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=     1 NaN    NaN   \n",
       "4  q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=     1 NaN    NaN   \n",
       "\n",
       "   registered_via  registration_init_time  expiration_date  \\\n",
       "0               7                20110820         20170920   \n",
       "1               7                20150628         20170622   \n",
       "2               4                20160411         20170712   \n",
       "3               9                20150906         20150907   \n",
       "4               4                20170126         20170613   \n",
       "\n",
       "   expiration_date_year  expiration_date_month expiration_date_Ym  use_days  \n",
       "0                  2017                      9             201709 2223 days  \n",
       "1                  2017                      6             201706  725 days  \n",
       "2                  2017                      7             201707  457 days  \n",
       "3                  2015                      9             201509    1 days  \n",
       "4                  2017                      6             201706  138 days  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算用户使用的天数\n",
    "members['use_days'] = pd.to_datetime(members['expiration_date'],format = '%Y%m%d',errors = 'ignore') \\\n",
    "                                - pd.to_datetime(members['registration_init_time'],format = '%Y%m%d',errors = 'ignore')\n",
    "members.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将使用天数转为int型数值\n",
    "members['use_days'] = members['use_days'].apply(lambda x:x.days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>gender</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>expiration_date_year</th>\n",
       "      <th>expiration_date_month</th>\n",
       "      <th>expiration_date_Ym</th>\n",
       "      <th>use_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20110820</td>\n",
       "      <td>20170920</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>201709</td>\n",
       "      <td>2223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20150628</td>\n",
       "      <td>20170622</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>201706</td>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20160411</td>\n",
       "      <td>20170712</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>201707</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>20150906</td>\n",
       "      <td>20150907</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>201509</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20170126</td>\n",
       "      <td>20170613</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>201706</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           msno  city  bd gender  \\\n",
       "0  XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=     1 NaN    NaN   \n",
       "1  UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=     1 NaN    NaN   \n",
       "2  D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=     1 NaN    NaN   \n",
       "3  mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=     1 NaN    NaN   \n",
       "4  q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=     1 NaN    NaN   \n",
       "\n",
       "   registered_via  registration_init_time  expiration_date  \\\n",
       "0               7                20110820         20170920   \n",
       "1               7                20150628         20170622   \n",
       "2               4                20160411         20170712   \n",
       "3               9                20150906         20150907   \n",
       "4               4                20170126         20170613   \n",
       "\n",
       "   expiration_date_year  expiration_date_month expiration_date_Ym  use_days  \n",
       "0                  2017                      9             201709      2223  \n",
       "1                  2017                      6             201706       725  \n",
       "2                  2017                      7             201707       457  \n",
       "3                  2015                      9             201509         1  \n",
       "4                  2017                      6             201706       138  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>expiration_date_year</th>\n",
       "      <th>expiration_date_month</th>\n",
       "      <th>use_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34403.000000</td>\n",
       "      <td>14440.000000</td>\n",
       "      <td>34403.000000</td>\n",
       "      <td>3.440300e+04</td>\n",
       "      <td>3.440300e+04</td>\n",
       "      <td>34403.000000</td>\n",
       "      <td>34403.000000</td>\n",
       "      <td>34403.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.371276</td>\n",
       "      <td>28.872438</td>\n",
       "      <td>5.953376</td>\n",
       "      <td>2.013994e+07</td>\n",
       "      <td>2.016902e+07</td>\n",
       "      <td>2016.823155</td>\n",
       "      <td>7.729442</td>\n",
       "      <td>1092.103101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.243929</td>\n",
       "      <td>9.076649</td>\n",
       "      <td>2.287534</td>\n",
       "      <td>2.954015e+04</td>\n",
       "      <td>6.872271e+03</td>\n",
       "      <td>0.694645</td>\n",
       "      <td>3.249376</td>\n",
       "      <td>1147.677034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.004033e+07</td>\n",
       "      <td>2.004102e+07</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.012103e+07</td>\n",
       "      <td>2.017020e+07</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.015090e+07</td>\n",
       "      <td>2.017091e+07</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>701.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.016110e+07</td>\n",
       "      <td>2.017093e+07</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1769.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.017023e+07</td>\n",
       "      <td>2.020102e+07</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5149.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               city            bd  registered_via  registration_init_time  \\\n",
       "count  34403.000000  14440.000000    34403.000000            3.440300e+04   \n",
       "mean       5.371276     28.872438        5.953376            2.013994e+07   \n",
       "std        6.243929      9.076649        2.287534            2.954015e+04   \n",
       "min        1.000000      3.000000        3.000000            2.004033e+07   \n",
       "25%        1.000000     22.000000        4.000000            2.012103e+07   \n",
       "50%        1.000000     27.000000        7.000000            2.015090e+07   \n",
       "75%       10.000000     34.000000        9.000000            2.016110e+07   \n",
       "max       22.000000     87.000000       16.000000            2.017023e+07   \n",
       "\n",
       "       expiration_date  expiration_date_year  expiration_date_month  \\\n",
       "count     3.440300e+04          34403.000000           34403.000000   \n",
       "mean      2.016902e+07           2016.823155               7.729442   \n",
       "std       6.872271e+03              0.694645               3.249376   \n",
       "min       2.004102e+07           2004.000000               1.000000   \n",
       "25%       2.017020e+07           2017.000000               6.000000   \n",
       "50%       2.017091e+07           2017.000000               9.000000   \n",
       "75%       2.017093e+07           2017.000000              10.000000   \n",
       "max       2.020102e+07           2020.000000              12.000000   \n",
       "\n",
       "           use_days  \n",
       "count  34403.000000  \n",
       "mean    1092.103101  \n",
       "std     1147.677034  \n",
       "min        0.000000  \n",
       "25%       72.000000  \n",
       "50%      701.000000  \n",
       "75%     1769.000000  \n",
       "max     5149.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2a0ce9b9668>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(members['use_days'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "members['expiration_date_Ym'] = members['expiration_date_Ym'].astype(object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34403 entries, 0 to 34402\n",
      "Data columns (total 11 columns):\n",
      "msno                      34403 non-null object\n",
      "city                      34403 non-null int64\n",
      "bd                        14440 non-null float64\n",
      "gender                    14501 non-null object\n",
      "registered_via            34403 non-null int64\n",
      "registration_init_time    34403 non-null int64\n",
      "expiration_date           34403 non-null int64\n",
      "expiration_date_year      34403 non-null int64\n",
      "expiration_date_month     34403 non-null int64\n",
      "expiration_date_Ym        34403 non-null object\n",
      "use_days                  34403 non-null int64\n",
      "dtypes: float64(1), int64(7), object(3)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "members.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "msno                          0\n",
       "city                          0\n",
       "bd                        19963\n",
       "gender                    19902\n",
       "registered_via                0\n",
       "registration_init_time        0\n",
       "expiration_date               0\n",
       "expiration_date_year          0\n",
       "expiration_date_month         0\n",
       "expiration_date_Ym            0\n",
       "use_days                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>msno</th>\n",
       "      <th>city</th>\n",
       "      <th>bd</th>\n",
       "      <th>gender</th>\n",
       "      <th>registered_via</th>\n",
       "      <th>registration_init_time</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>expiration_date_year</th>\n",
       "      <th>expiration_date_month</th>\n",
       "      <th>expiration_date_Ym</th>\n",
       "      <th>use_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20110820</td>\n",
       "      <td>20170920</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>201709</td>\n",
       "      <td>2223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>20150628</td>\n",
       "      <td>20170622</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>201706</td>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20160411</td>\n",
       "      <td>20170712</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>201707</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>20150906</td>\n",
       "      <td>20150907</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>201509</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>20170126</td>\n",
       "      <td>20170613</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>201706</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           msno  city  bd gender  \\\n",
       "0  XQxgAYj3klVKjR3oxPPXYYFp4soD4TuBghkhMTD4oTw=     1 NaN    NaN   \n",
       "1  UizsfmJb9mV54qE9hCYyU07Va97c0lCRLEQX3ae+ztM=     1 NaN    NaN   \n",
       "2  D8nEhsIOBSoE6VthTaqDX8U6lqjJ7dLdr72mOyLya2A=     1 NaN    NaN   \n",
       "3  mCuD+tZ1hERA/o5GPqk38e041J8ZsBaLcu7nGoIIvhI=     1 NaN    NaN   \n",
       "4  q4HRBfVSssAFS9iRfxWrohxuk9kCYMKjHOEagUMV6rQ=     1 NaN    NaN   \n",
       "\n",
       "   registered_via  registration_init_time  expiration_date  \\\n",
       "0               7                20110820         20170920   \n",
       "1               7                20150628         20170622   \n",
       "2               4                20160411         20170712   \n",
       "3               9                20150906         20150907   \n",
       "4               4                20170126         20170613   \n",
       "\n",
       "   expiration_date_year  expiration_date_month expiration_date_Ym  use_days  \n",
       "0                  2017                      9             201709      2223  \n",
       "1                  2017                      6             201706       725  \n",
       "2                  2017                      7             201707       457  \n",
       "3                  2015                      9             201509         1  \n",
       "4                  2017                      6             201706       138  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "members.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "members.to_csv('FE_members.csv',index = False)"
   ]
  }
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